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EURASIP JASP Special Issue on Machine Learning in Image Processing
Anonymous writes "EURASIP JASP Special Issue on Machine Learning in Image Processing
URL : http://www.hindawi.com/journals/asp/si/mlip.html
Call for papers
Images have always played an important role in human life since vision probably is human beings' most important sense. As a consequence, the field of image processing has numerous applications (medical, military, etc.). Nowadays and more than ever, images are everywhere and it is very easy for everyone to generate a huge amount of images thanks to the advances in digital technologies. With such a profusion of images, traditional image processing techniques have to cope with more complex problems and have to face with their adaptability according to human vision. Vision being complex, machine learning has emerged as a key component of intelligent computer vision programs when adaptation is needed (e.g., face recognition) . Among the existing methods, one can quote neural networks, hidden Markov models, kernel based methods, and so forth. However, this mainly concerns the computer vision field, the learning of which emulates high-level vision processes (e.g., visual information categorization or interpretation). But one can also incorporate learning in image processing to emulate low-level vision processes. We can quote edge detection, noise filtering, adaptive compression, and so on, as such potential issues. With the advent of image datasets and benchmarks, machine learning and image processing have recently received a lot of attention. An innovative integration of machine learning in image processing is very likely to have a great benefit to the field, which will contribute to a better understanding of complex images. The number of image processing algorithms that incorporate some learning components is expected to increase, as adaptation is needed. However, an increase in adaptation is often linked to an increase in complexity and one has to efficiently control any machine learning technique to properly adapt it to image processing problems. Indeed, processing huge amounts of images means being able to process huge quantities of data often of high dimensions, which is problematic for most machine learning techniques. Therefore, an interaction with the image data and with image priors is necessary to drive model selection strategies.
The primary purpose of this special issue is to increase the awareness of image processing researchers to the impact of machine learning algorithms in low-level tasks. Papers submitted to this special issue have to carefully address the problem of model selection (features selection, parameter or hyperparameters estimation) for the machine learning technique under consideration.
This special issue aims at providing original and high-quality submissions related, but not limited, to one or more of the following topics:
* Machine learning in image filtering
* Machine learning in image restoration
* Machine learning in edge detection
* Machine learning in image feature extraction
* Machine learning in image segmentation
* Machine learning in image compression
* Machine learning driven by imaging applications.
Moreover, since image databases created for benchmarking or for training are crucial for progress in both machine learning and image processing fields, the evaluation of the submitted papers will take that aspect into account (accessibility, quality, reproducibility) and the performance evaluation has to be carefully adressed.
Authors should follow the EURASIP Journal on Advances in Signal Processing manuscript format described at the journal site http://www.hindawi.com/journals/asp/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://www.hindawi.com/mts/ according to the following timetable:
Manuscript Due September 1, 2007
First Round of Reviews December 1, 2007
Publication Date March 1, 2008
Guest Editors:
Olivier Lezoray, Vision and Image Analysis (VAI) Team,Cherbourg Applied Sciences University Laboratory (LUSAC), 120 Rue de l'Exode, 50000 Saint-Lô, France
Christophe Charrier, Vision and Image Analysis (VAI) Team, Cherbourg Applied Sciences University Laboratory (LUSAC), 120 Rue de l'Exode, 50000 Saint-Lô, France
Hubert Cardot, Pattern Recognition and Image Analysis Team, Computer Science Laboratory (LI), Université François Rabelais de Tours, 64 avenue Jean Portalis, 37200 Tours, France
Sébastien Lefèvre, Models Images Vision (MIV) Team, Image Sciences, Computer Sciences and Remote Sensing Laboratory (LSIIT), CNRS and Louis Pasteur University (Strasbourg), Pôle API, Bd. Brant, BP 10413, 67412 Illkirch, France"
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Posted by schestowitzon Tuesday, May 15 @ 03:35:51 UTC (3576 reads)
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Machine Learning Department
As it turns out, machine learning has become a broad enough field to have its own specialised department at Carnegie Mellon. It will become the first machine learning department in the States.
March 30, 2006, PITTSBURGH—Carnegie Mellon University’s School of Computer Science (SCS) is extending its leadership in the burgeoning field of machine learning by creating the nation’s first Machine Learning Department.
Full story
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Posted by schestowitzon Friday, March 31 @ 06:15:52 UTC (1169 reads)
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